{"id":"W4306827332","doi":"10.1016/j.jcmg.2022.07.017","title":"Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning","year":2022,"lang":"en","type":"article","venue":"JACC. Cardiovascular imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"National Heart, Lung, and Blood Institute","keywords":"Mace; Medicine; Myocardial perfusion imaging; Myocardial infarction; Internal medicine; Quartile; Cardiology; Area under the curve; Perfusion; Receiver operating characteristic; Logistic regression; Perfusion scanning; Confidence interval; Percutaneous coronary intervention","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001028344,0.001244542,0.0008279139,0.0009512333,0.0002361385,0.0009831795,0.0009957431,0.001278024,0.003230541],"category_scores_gemma":[0.005862092,0.0004158705,0.0007257768,0.0003941318,0.0003224857,0.00093312,0.001167577,0.001745475,0.0006444852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005126885,"about_ca_system_score_gemma":0.000916518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004648542,"about_ca_topic_score_gemma":0.00591593,"domain_scores_codex":[0.9996845,0.0001047479,0.00001595763,0.00008934505,0.00005990123,0.00004556588],"domain_scores_gemma":[0.9983247,0.001156898,0.0001363273,0.0001214444,0.0001734991,0.00008701381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001067295,0.0003784382,0.03120678,0.0003274424,0.0004725306,0.0007993754,0.0001142545,0.4619718,0.008162358,0.01331298,0.02210437,0.4600823],"study_design_scores_gemma":[0.00002662524,0.00005738665,0.001298782,0.00002672754,0.00003814959,0.00009474163,0.0000098175,0.9777927,0.001411483,0.01848342,0.0007485601,0.00001151108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09107116,0.001620308,0.8986852,0.002363212,0.000137619,0.00006683493,0.001537537,0.002141186,0.002376881],"genre_scores_gemma":[0.9198999,0.0006645762,0.07369642,0.0004579881,0.0002663527,0.000081873,0.001674872,0.000164421,0.003093492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004648542,"threshold_uncertainty_score":0.01080722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008461413484230108,"score_gpt":0.2499992180184147,"score_spread":0.2415378045341846,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}